US10143389B2ActiveUtilityPatentIndex 58
Distinguishing diseased tissue from healthy tissue based on tissue component fractions using magnetic resonance fingerprinting (MRF)
Est. expiryApr 22, 2034(~7.8 yrs left)· nominal 20-yr term from priority
A61B 5/055G01R 33/56563A61B 5/0263G01R 33/448A61B 1/00G01R 33/4828G01R 1/00G01R 33/5602G01R 33/5608G01R 33/5611A61B 5/742A61B 2217/00G01R 33/50A61B 5/4064A61B 2218/00G01R 33/56366G01R 33/4818G06T 2200/00G06T 1/00A61B 17/00
58
PatentIndex Score
1
Cited by
4
References
26
Claims
Abstract
Example embodiments associated with characterizing a sample using NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. The NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and a characterization logic that characterizes a tissue in the object as a result of comparing acquired signals to reference signals. Example embodiments facilitate distinguishing diseased tissue from healthy tissue based on tissue component fractions identified using the NMR fingerprinting.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1. A method, comprising:
accessing a set of known magnetic resonance fingerprinting (MRF) signal evolutions,
where a member of the set of known MRF signal evolutions was produced by combining data associated with nuclear magnetic resonance (NMR) signals associated with a plurality of resonant species,
where combining the data involves using a forward operation for which an inverse operation that identifies relative fractions of resonant species associated with the member is available;
accessing an acquired NMR signal, where the acquired NMR signal was produced by a volume that contains one or more resonant species, where the one or more resonant species simultaneously produced individual NMR signals in response to MRF excitation produced by an MRF pulse sequence; and
performing the inverse operation on the selected entry to determine relative fractions of the one or more resonant species for the volume.
2. The method of claim 1 , comprising:
identifying the volume as containing tissue that is a member of a tissue category based, at least in part, on the relative fractions, and
providing information concerning the relative fractions or providing information concerning the tissue category.
3. The method of claim 1 , where the relative fractions concern normal white matter, normal appearing but abnormal white matter associated with multiple sclerosis, normal grey matter, normal appearing but abnormal grey matter associated with multiple sclerosis, or a lesion associated with multiple sclerosis.
4. The method of claim 2 , where the tissue category is tissue exhibiting indicia of multiple sclerosis or tissue not exhibiting indicia of multiple sclerosis.
5. The method of claim 1 , comprising producing the set of known MRF signal evolutions.
6. The method of claim 5 , where producing the set of known MRF signal evolutions includes combining data associated with NMR signals associated with the plurality of resonant species using a weighted sum operation.
7. The method of claim 6 , where the inverse operation is a matrix pseudo-inverse operation.
8. The method of claim 5 , where producing the set of known MRF signal evolutions includes producing data associated with NMR signals by varying one or more first parameters associated with one or more first resonant species contributing to the NMR signals while holding constant one or more second parameters associated with one or more second resonant species contributing to the NMR signals.
9. The method of claim 8 , where the one or more first parameters include T1 and T2, and where the one or more second parameters include T1 and T2, T1 being spin-lattice relaxation, T2 being spin-spin relaxation.
10. The method of claim 9 , where the one or more first resonant species include fluids found in a human brain, cells found in a human brain, water interacting with cellular membranes found in a human brain, and myelin water.
11. The method of claim 10 , where the one or more second resonant species include myelin water.
12. The method of claim 5 , where producing the set of known MRF signal evolutions includes producing data associated with models of signal evolutions by:
holding T1 and T2 constant for a first resonant species,
holding T1 and T2 constant for a second resonant species,
holding T2 constant for a third resonant species, and
varying T1 for the third resonant species,
T1 being spin-lattice relaxation, T2 being spin-spin relaxation.
13. The method of claim 12 , where T1 is varied from 100 μs to 5000 ms for the third component.
14. The method of claim 13 , where the first resonant species is fluids found in the human brain, where the second resonant species is water interacting with cellular membranes found in the human brain, and where the third resonant species is myelin water.
15. The method of claim 14 , comprising distinguishing diseased tissue from healthy tissue as a function of plotting myelin water as a function of T1.
16. The method of claim 1 , comprising:
controlling an MRF apparatus to produce the MRF excitation using the MRF pulse sequence by applying radio frequency (RF) energy to the volume in an object in a series of variable sequence blocks, where a sequence block includes one or more excitation phases, one or more readout phases, and one or more waiting phases,
where the RF energy applied during a sequence block causes the one or more resonant species in the volume to simultaneously produce individual NMR signals, and
where at least one member of the series of variable sequence blocks differs from at least one other member of the series of variable sequence blocks in at least N sequence block parameters, N being an integer greater than one, and
controlling the MRF apparatus to acquire the simultaneously produced individual NMR signals.
17. The method of claim 16 , where the sequence block parameters include echo time, flip angle, phase encoding, diffusion encoding, flow encoding, RF pulse amplitude, RF pulse phase, number of RF pulses, type of gradient applied between an excitation portion of a sequence block and a readout portion of a sequence block, number of gradients applied between an excitation portion of a sequence block and a readout portion of a sequence block, an amount by which a gradient is unbalanced when applied between an excitation portion of a sequence block and a readout portion of a sequence bock, a type of gradient applied between a readout portion of a sequence block and an excitation portion of a sequence block, a number of gradients applied between a readout portion of a sequence block and an excitation portion of a sequence block, an amount by which a gradient is unbalanced when applied between a readout portion of a sequence block and an excitation portion of a sequence bock, a type of gradient applied during a readout portion of a sequence block, a number of gradients applied during a readout portion of a sequence block, an amount of RF spoiling, or an amount of gradient spoiling.
18. The method of claim 17 , comprising:
controlling the MRF apparatus to vary an amount of time between sequence blocks in the series of variable sequence blocks, the relative amplitude of RF pulses in sequence blocks in the series of variable sequence blocks, or the relative phase of RF pulses in sequence blocks in the series of variable sequence blocks.
19. The method of claim 1 , where the set of known signal evolutions includes signal evolutions outside the set of signal evolutions characterized by:
SE= A -Be −t/C
where:
SE is a signal evolution,
A is a constant,
B is a constant,
t is time, and
C is a single relaxation parameter.
20. The method of claim 1 , where the set of known signal evolutions includes a signal selected from a set of signals described by:
S
E
=
∑
i
=
1
N
S
∏
i
=
1
N
A
∑
j
=
1
N
RF
R
i
(
α
)
R
RF
ij
(
α
,
ϕ
)
R
(
G
)
E
i
(
T
1
,
T
2
,
K
)
DPdM
0
or
S
E
=
∑
i
=
1
N
S
∏
i
=
1
N
A
∑
j
=
1
N
RF
R
i
(
α
)
R
RF
ij
(
α
,
ϕ
)
R
(
G
)
E
i
(
T
1
,
T
2
,
K
)
DPdM
0
where:
SE is a signal evolution,
N s is a number of spins,
N A is a number of sequence blocks,
N RF is a number of RF pulses in a sequence block,
is a flip angle,
ϕ is a phase angle,
Ri( ) is a rotation due to off resonance,
R RFij ( ,ϕ) is a rotation due to RF differences,
R(G) is a rotation due to a gradient,
T1 is spin-lattice relaxation,
T2 is spin-spin relaxation,
D is diffusion relaxation,
Pd is proton density,
E i (T1,T2, . . .) is decay due to relaxation differences, and
M 0 is the default or equilibrium magnetization.
21. The method of claim 1 , where the set of known signal evolutions includes a signal selected from a set of signals described by:
S i =R i E i ( S i−1 )
where:
S 0 is the default or equilibrium magnetization,
S i is a vector that represents the different components of the magnetization Mx, My, Mz during acquisition block i,
R i is the combination of rotational effects that occur during acquisition block i, and
E i is the combination of effects that alter the amount of magnetization in the different states for acquisition block i.
22. The method of claim 1 , where the set of known signal evolutions includes a signal selected from a set of signals described by:
S
i
=
R
i
E
i
∑
x
=
1
i
-
1
R
x
E
x
(
S
x
)
or
S
i
=
R
i
E
i
∏
x
=
1
i
-
1
R
x
E
x
(
S
x
)
where:
S 0 is the default or equilibrium magnetization,
S i is a vector that represents the different components of the magnetization Mx, My, Mz during acquisition block i,
R i is the combination of rotational effects that occur during acquisition block i, and
E i is the combination of effects that alter the amount of magnetization in the different states for acquisition block i.
23. The method of claim 1 , where the set of known signal evolutions includes a signal selected from a set of signals described by:
S
i
=
∑
s
=
1
N
s
R
s
,
i
E
s
,
i
(
S
s
,
i
-
1
)
where:
S 0 is the default or equilibrium magnetization,
Ns is the number of spins,
S i is a vector that represents the different components of the magnetization Mx, My, Mz during acquisition block i,
R i is the combination of rotational effects that occur during acquisition block i, and
E i is the combination of effects that alter the amount of magnetization in the different states for acquisition block i.
24. The method of claim 1 , where the set of known signal evolutions includes a signal selected from a set of signals described by:
S
i
=
∑
s
=
1
N
s
R
s
,
i
E
s
,
i
∑
x
=
1
i
-
1
R
s
,
x
E
s
,
x
(
S
s
,
x
)
or
S
i
=
∑
s
=
1
N
s
R
s
,
i
E
s
,
i
∏
x
=
1
i
-
1
R
s
,
x
E
s
,
x
(
S
s
,
x
)
where:
S 0 is the default or equilibrium magnetization,
Ns is the number of spins,
S i is a vector that represents the different components of the magnetization Mx, My, Mz during acquisition block i,
R i is the combination of rotational effects that occur during acquisition block i, and
E i is the combination of effects that alter the amount of magnetization in the different states for acquisition block i.
25. The method of claim 1 , comprising producing an image based, at least in part, on quantitative values associated with the selected entry in the set of known MRF signal evolutions and the resonant species fractions.
26. An apparatus, comprising:
a nuclear magnetic resonance (NMR) logic that receives a first set of data from a magnetic resonance fingerprinting (MRF) apparatus that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals,
where the MRF apparatus applies radio frequency (RF) energy to the object according to an MRF pulse sequence to cause the object to produce the set of NMR signals,
where members of the first set of data are associated with different points in the (k, t, E) space, where t is time and E includes at least T1 and T2, T1 being spin-lattice relaxation and T2being spin-spin relaxation, and where one or more of, t and E, vary non-linearly;
a signal logic that produces an NMR signal evolution from the first set of data;
a matching logic that selects, from a collection of stored signal evolutions, a selected stored signal evolution that matches the NMR signal evolution to within a desired tolerance,
where information concerning relative proportions of resonant species that contributed to the selected stored signal evolution is retrievable using the selected stored signal evolution.Cited by (0)
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